USGIF GotGeoint BlogUSGIF promotes geospatial intelligence tradecraft and a stronger community of interest between government, industry, academia, professional organizations and individuals focused on the development and application of geospatial intelligence to address national security objectives.

Mark Enzer, chief technology officer at Mott MacDonald UK and proposed chair of the Digital Framework Task Group (DFTG) which is tasked to drive these reports’ implementation forward in his keynote at Geo Business 2018 in London, explained that there will be a greater focus on existing infrastructure. Digital abundance where the cost of everything digital has dropped dramatically over the last couple of decades has transformed many industries from banks to airlines automobile manufacturing. Construction represents about 10 % of GDP and construction productivity has plateaued over the past 40 years whereas general industrial productivity has doubled. Mark pointed out that the UK has primarily mature infrastructure to maintain and operate and construction has to change to reflect this.

Based on the concept that data in the form of a national digital twin is just as important as phyical assets, digital delivery and physical delivery go hand in hand. Key to digital delivery are BIM, geospatial, a common data environment, and asset information management. A national digital twin would include above and below ground assets.

Managing this data is about making sense of it for better decision making. Fundamental to this process is rethinking value, not just the value of a finished building or infrastructure asset, but output per ‎£ over the entire lifecycle of an infrastructure asset. This means moving beyond BIM Level 2 to full lifecyle BIM including operate and maintain.

A coordinated digital transformation landscape is required to achieve this digital transformation strategy for economic transformation. He sees the many organization in the UK infrastructure and construction sector coalescing around ICG, representing Highways England, Network Rail, Crossrail, Highspeed Rail 2 (HS2), Heathrow Airport and others and Centre for Digital Built Britain (CDBB) representing the UK BIM Alliance and others to enable this to happen. The Centre for Digital Built Britain is a partnership between the Department of Business, Energy & Industrial Strategy and the University of Cambridge to deliver a smart digital economy for infrastructure and construction for the future and transform the UK construction industry’s approach to the way the UK plans, builds, maintains and uses its social and economic infrastructure.

May 30, 2018

Deep learning algorithms have been developed by academia and as a result the code for the most part is open source but the successful training of deep networks requires thousands of labeled training samples and at the present time this training data is typically not open. In their presentation at this year's FOSS4GNA (Free and Open Source Software for Geospatial North America) get together in St Louis Jason Brown and Courtney Whalen, data scientists at astraea, showed how deep learning using publicly available labeled data for training was applied to track deforestation and reforestation in Mato Grosso, a state in the central amazon region of Brazil.

Chris Holmes of Planet Labs, in his insightful talk at FOSS4GNA in St Louis about the application of deep learning to geospatial data, identified a challenge in making this technology open. The deep learning algorithms have been developed by academia and as a result the code for the most part is open source. For example, a deep neural network model developed originally for medical image segmentation called U-Net is open source and has been applied to identifying building footprints. Successful training of deep networks requires thousands of labeled training samples. Labeled data involves people on the ground manually ground-truthing land use types and other features so that the deep learning algorithms can learn what to recognize. At the present time this training data is typically not open source. In this presentation by Jason Brown and Courtney Whalen, both data scientists at astraea, deep learning using publicly available labeled data for training was used to track deforestation and reforestation in Mato Grosso, a state in the central amazon region of Brazil.

This is computationally intensive and a distributed engine was used. The computation engine used open source components. Spark is a top level Apache project which enables distributed processing for global scale computation. RasterFrames is a free and open source toolkit allowing scientists, data scientists, and software developers to process and analyze geo

Forest cover in Mato Grosso in 2002

patial-temporal raster data with the same flexibility and ease as any other data type in Spark DataFrames. This is a LocationTech raster project and is built on GeoTrellis. Using this software each year required 6 to 7 hours of computation using 48 cores.

The imagery that was used was captured by the MODIS satellite for the years 2001 through 2017. MODIS monitors the reflection back from ground cover for several bands including red, green, blue, short wave infrared and near infrared. Its cameras have a spatial resolution of 500 by 500 meters and a revisit rate of one to two days. From the bands that it captures the normalized difference vegetation index (NDVI) can be calculated. From the data monthly means and yearly aggregates can be calculated.

Forest cover in Mato Grosso in 2011

The training data used came from the System for Terrrestrial Ecosystem Parameterization (STEP) which has 2000 manually labeled sites covering 17 different land cover types including five forest types scattered across all continents. The model was trained on MODIS 2012 data. 80% of the data was used for training. After training was completed the remaining 20% was used to test the model.

Comparison of rate of forest loss with Global Forest Watch for 2001 to 2016

After training and testing the first application was to Mato Grosso in central Brazil, a large state that has seen a lot of deforestation. The rate of deforestation tracked the rate estimated independently by the Global Forest Watch for the years 2001 to 2017. The major feature, the slowing down of the rate of deforestation in 2011 probably as a result of increased enforcement by the state government, is very clearly discernible.

The successful application of the deep learning technology in Mato Grosso has encouraged astraea to aim at applying this approach globally. They also intend to use satellite data with higher resolution and to handle seasonal differences better.

About STEP

The System for Terrestrial Ecosystem Parameterization (STEP) is a model for deriving vegetation and land surface parameters from remote sensing data for use in remote sensing-based classification of land cover, ecosystems, and vegetation types. The model defines parameters that relate to important ecological and biogeophysical parameters and that can be reliably measured or inferred from remote sensing, collateral, and field plot data. STEP is maintained as a database of training polygons drawn on high spatial resolution imagery that can be extracted with GIS to produce a global land cover classification. STEP is periodically reviewed to filter out inconsistent sites and augmented to fill gaps in biogeographical coverage. The database was originally created to follow the International Geosphere-Biosphere Programme (IGBP) land cover legend but it has since evolved to support any number of additional classifications.

May 28, 2018

Governments are consolidating the role of geospatial data and technology in central governments. I have blogged about the Geospatial Data Act of 2017 in the U.S. Studies of the value of spatial data and technology to several national economies have suggested that wise government policy could significantly increase the contribution of spatial data and technology to the national GDP. Earlier this year the UK government created a Geospatial Commission which is intended to consolidate ten government departments and agencies including the Ordnance Survey. At Geo Business 2018 in London, William Priest, Director of the Geospatial Commission, gave an overview of the mission of the Geospatial Commission and the role it could play in the digital transformation of construction.

‎ Geospatial data and technology has disrupted things like nothing else. In the hands of Google, Uber, Tesla, Apple, Amazon, Twitter, and others geospatial data and technology have touched just about everyone's lives. And the UK has been a leader in the application geospatial data and technology. Among the countries of the world the UK ranks #1 or #2 in geospatial readiness. The Geospatial Commission has been allocated GBP 40 million‎ for year 1 and year 2 to find ways to use geospatial technology to deliver economic growth and improve productivity and drive investment to foster innovation and to protect and enhance the quality of the UK's world class geospatial data assets. If this money is spent money wisely, it could unlock tremendous value in the UK economy, primarily by removing removing barriers, for example, in making the Ordnance Survey's Mastermap more open and accessible.

Of the potential use cases where geospatial data and technology could unlock value - healthcare and social welfare, housing, land and planning, optimizing end-to-end supply chains, infrastructure and construction, forecasting demand for public services, data quality and standards, flood prevention and protection, earth observation, and critical national infrastructure - the one that could have the largest impact on the economy is construction which represents roughly 10 % of GDP. In a recent report, McKinsey & Company suggests that the construction industry is ripe for disruption and one of the five technologies that it identified as key in the anticipated transformation is geospatial.

May 25, 2018

The General Data Protection Regulation (GDPR) enters into force today. It is a regulation in EU law on data protection and privacy for all individuals within the European Union and the European Economic Area. It also addresses the export of personal data outside the EU and EEA. The GDPR is quite far ranging. Here are some interesting examples of what it covers. You can take the online test that the BBC prepared (which the material below is derived from) here. You will notice that location data is included in the personal data covered by the legislation. Surprising to me is that it covers AI (artificial intelligence) when it is applied to an application.

GDPR is designed to help people protect and control use of their "personal data". What does that cover?

The UK’s Information Commissioner’s Office defines personal data as: "Information relating to an identifiable person who can be directly or indirectly identified in particular by reference to an identifier." It says this includes "name, identification number, location data or online identifier". Under some circumstances, this can extend to images, and details about your family. Unconnected facts - such as the distance from the Earth to the Moon - would not become personal data just on your say-so.

A free app that relies on adverts to make money has gathered information about you. Under what circumstances can you forbid it to use the data?

Organizations have six lawful bases for processing personal data: Consent, contract, legal obligation, vital interests, public task or legitimate interest. But whatever the legal basis, you always have the right to object to the continued processing of your personal data if it's for the purposes of direct marketing. If you willingly and explicitly consented to your personal data being used for ads in the past, then apps and others can continue to do so.

An online chat service wants access to your location and email contacts and is relying on user consent as the legal basis for this.

GDPR says consent means individuals must have real choice and control. So, they need to have a "clear and concise" explanation as to what they are agreeing to, and pre-ticked boxes and other forms of default consent no longer apply.

Over time you have grown to dislike being part of a social network and decide to quit.

GDPR introduces a right for individuals to demand their personal data be erased under some circumstances. These include situations when its use has been based on their consent. Organizations must respond within a month of receiving the request and should comply without charging a fee unless the request is deemed "manifestly unfounded or excessive".

You are unconscious after a car crash and require surgery. Can your GP provide your personal medical records to the nearest hospital ?

One basis for processing and sharing an individual’s personal data is that it is necessary to protect their vital interests, which includes saving their life. So, sharing data with a hospital's A&E department for an emergency would count.

A video streaming service emailed you all the personal data it held about you last week, as requested, but you decide to ask again.

GDPR gives individuals a right to access the personal data held on them under some circumstances. Organizations are encouraged to make this possible via the internet, and are supposed to respond within a month. However, if a user subsequently asks to be sent further copies of their data, then the service involved can charge a "reasonable fee" based on its administrative costs.

You lose your wallet. Inside is a scrap of paper on which you had written down your work login and current password. Your IT department confirms that an unidentified party entered your account, giving them access to a file containing the names and addresses of 12 police informants involved in a project you are working on.

GDPR introduces a duty to report certain types of data breaches within 72 hours of them being detected, even if all the details are not yet known. If individuals are also put at significant risk, they must also be informed. Failure to comply can entail a fine of up to $23.6 million or 4% of the annual global turnover.

Your application to work at a restaurant is turned down, and you are told it was rejected by the firm's artificial intelligence system.

GDPR gives an individual the right to challenge decisions made solely on the automated analysis of their personal data if they did not consent to it in advance. Those affected can ask for access to the details on which the decision was based. They also have the right to have a human double-check that a mistake was not made.

You decide to quit membership of a gym chain to join a rival. You ask for your data in a common machine readable format.

GDPR includes the right to obtain and reuse personal data from one service to another. It applies when the lawful basis for processing the information is consent or contract, and the processing is carried out by automated means (ie not paper files). The data must be provided in a commonly used and machine-readable format.

May 24, 2018

The British standard PAS 128 which defines quality levels for the location of underground utilities was released in 2014. It is now up for revision. At Geo Business 2018 in London, Ian Bush of Black and Veatch, provided background to the PAS128 standard and identified the issues that have motivated the revision initiative.

The PAS process is a sponsored fast track specification managed and produced by the British Standards Institution (BSI). The PAS process requires 12 to 16 months from initiation to publication and follows rigorous rules specified by the BSI.

PAS128 is entirely the work of volunteers, but using BSI requires support. Historically, the development of the PAS128 standard for locating underground utilities was sponsored by many organizations in the UK including the Institution of Civil Engineers (ICE), Heathrow Airport Holdings, Highways Agency, Transport for London, National Joint Utilities Group, Ordnance Survey, University of Birmingham - School of Civil Engineering, the Utility Mapping Association and others.

PAS 128, which was aimed at practitioners not clients, was initiated in 2012 and went through 3 drafts. There was a high level of participation in the industry. The first draft got 508 and the second 685 comments. The final draft was published in 2014.It describes a hierarchy of quality levels similar to the American Society of Civil Engineers (ASCE) standard

C Site reconnaissance - visiting the site and indentifying relevant surface feature.

B Detection - using ground penetrating radar and electromagnetic detection and possibly other remote sensing technologies. It also included an absolute precision B1 to B3.

A Verification - using safe excavation tools on site to dig and find the utilities.

Ian said that the PAS128 standard has been very successful. Over 400 copies have been sold, which is exceptional for BSI publications which typically sell on the order of 200. It has been adopted by Hong Kong, El Salvador, and countries in the Middle East. But it needs updating to take advantage of new research such as the Mapping the Underworld and Assessing the Underworld projects and new detection and survey methods such as gyroscopic mapping.

It also needs to address some serious issues. It needs to provide guidelines for clients, the current standard is addressed to practitioners. The current standard assumed a 2D world. The revision needs to focus more on 3D and BIM. One of the major issues, whether to include post-processing of GPR scans, needs to be revisited. There are inconsistencies between the recently released PAS256 and PAS128.

To support the revision, which will again be performed by volunteers, Ian is looking for sponsors to fund the use of BSI and people to join the review panel.

May 23, 2018

The existing documentation about the location of underground utilities is poor in many countries and the costs associated with utility strikes, hitting a utility such as a fiber-optic cable, electric power cable, or a gas or water main during construction is high. Therefore, finding a way to improve our knowledge about the location of underground subsurface infrastructure is essential, but to be practical it has to be a way that does not add significantly to the cost of construction. At Geo Business 2018, Richard Bath, a surveyor at Costain, described an efficient way of capturing enough information with an ordinary smart phone to accurately survey, map and make available via the web the location of utilities encountered during a construction project.

About 4 million excavations are carried out on the UK road network each year to install or repair buried utility pipes and cables. Not knowing the location of buried assets causes practical problems that increase costs and delay projects, but more importantly, it increases the risk of injury for utility owners, contractors and road users. The problems associated with inaccurate location of buried pipes and cables are serious and are rapidly worsening due to the increasing density of underground infrastructure in major urban areas. The average direct cost of hitting an underground utility during construction ranges from £400 to £2800 depending on the type of utility. Research has found that the total cost of a utility strike in the UK is on average 29 times the direct cost.

Performing a survey of utilities exposed during a construction project using a laser scanner and total station can be cost prohibitive and as a consequence is rarely done. A research project involving Costain and Bentley has found that photogrammetry using a consumer grade smartphone results in a 3D model of comparable accuracy to a laser scan survey and is much more cost efficient. They have also found a way to make 3D models created from the pictures captured with a smartphone available over the web.

Richard described a simple workflow to capture enough information with a smart phone to accurately determine the location of underground utilities.

Step 1 Mark ground control points (GCPs) around the area. They have to be visible in the pictures taken with the smartphone.

Step 2 Take pictures from varying angles and heights around the exposed utilities with a smartphone.

Step 3 Survey the GCPs with a total station, at least three are needed to accurately determine the location of the 3D model created from the pictures.

Step 4 Upload the photos taken with the smartphone and process them and GCPs together to create a georeferenced 3D model of the exposed utilities.

This process has been found to result in a 3D model of comparable accuracy to a full laser scan survey and unlike a laser scan survey it is something that anybody can do. Typically it involves taking 40-60 pictures with an ordinary smartphone. After uploading the pictures, processing them with Bentley's ContextCapture software to create the georeferenced 3D model, the resulting 3D model can be made available to others on the construction project using an online web link that allows the 3D model to be displayed along with existing 2D utility as-builts.

May 18, 2018

The World Health Organization (WHO) estimates that more than seven million deaths every year are linked to air pollution exposure from household and ambient (outdoor) air pollution. But in spite of these statistics the average city has only one or two air quality monitoring devices which are very expensive costing about $1.5 million each. At this year's FOSS4GNA (Free and Open Source Software for Geospatial North America) get together in St Louis Steve Liang, a professor at the University of Calagary and CEO of SensorUp, described a way that citizen scientists can contribute to measuring air pollution using a low cost board and CPU and share via open source geospatial web software to map the resulting measured air pollution in real time.

Based on the latest results from WHO air pollution is now the world’s largest single environmental health risk, linked to 12% of all global deaths. Around 4.3 million deaths every year are attributed to exposure to household (indoor) air pollution, from heating, cooking and lighting using solid fuels. Around 3.7 million deaths every year are linked to outdoor air pollution , including exposure to fine particulate matter from fuel combustion from vehicles and from power plants, industry and biomass burning.

Steve described the sensors, which are typically built by interested people in workshops led by staff from SensorUp, a startup that Steve leads. The devices consist of a sensor that measures PM2.5 (particles less than 2.5 microns in size), temperature and humidity, a CPU and Wifi. Each device costs less than $100. After building the device, each participant in the workshop brings it home and installs it outside within wifi range of their internet router. The sensor reports ambient PM2.5, temperature and humidity every 5 minutes to a central server maintained by SensorUp. It uses the Open Geospatial Consortium (OGC) standard SensorThings API which provides an open and unified way to interconnect Internet of Things (IoT) devices, data, and applications over the Web. SensorUp SensorThings platform is the most advanced OGC SensorThings API implementation.

A web application built on the open source geospatial software Leaflet allows users to view the data on a map in real time, investigate air pollution historically and compare different cities. The data is open and accessible to anyone. To date about 500 people across Canada have built their own devices and are sharing measured PM2.5. The real-time feed from these devices is mapped here.

May 17, 2018

One of the things that is required to make the vast quantity of satellite imagery easily searchable is a common way to query satellite data. The SpatioTemporal Asset Catalog, known as STAC, is an open specification that came about when fourteen different organizations came together to increase the interoperability of searching for satellite imagery. At this year's FOSS4GNA (Free and Open Source Software for Geospatial North America) get together in St Louis, Matt Hanson, of developmentSEED, gave a technical overview of the STAC standard and described one of the first implementations. The context for Matt's STAC presentation was provided by Chris Holmes' keynote in the morning.

Chris Holmes and others have been working on a standard for searching satellite imagery. Currently when a user wants to search for all the imagery in their area and time of interest they can’t make just one search — they have to use different tools and connect to API’s that are similar but all slightly different. The STAC specification aims to make that much easier, by providing common metadata and API mechanics to search and access geospatial data. This and other standards are essential for opening up satellite data to processing and visualization. This is one of a new breed of standards that are designed to be developer-friendly to encourage the open source community and others to get involved with the Open Geospatial Consortium (OGC) to further interoperability.

The STAC standard is minimalist, but balances that with extensibility. Basically it is comprised of metadata, catalog, and API. The core of STAC metadata is very simple, with only three mandatory fields; ID, geometry (for example, a bounding box in a defined projection), and date and time. The standard supports extensions, of which the earth observation standard "eo" has been defined at this time. Future extensions could include point cloud, mosaic and video extensions. The eo extension includes parameters such as camera viewing angle, resolution (dist between pixels), percent cloud cover, and band descriptions such as RGB, SWIR, band frequency and band accuracy.

The STAC standard and cloud optimized GeoTiff (COG) files makes it possible to search and stream imagery. COGs are GeoTiff files optimized for the cloud, for example, by streaming them from Amazon Web Services S3. Planet, Google Earth, and QGIS already support COGs and there are other open source tools such as COG-Explorer that support them as well.

Sat-api is developmentSEED's partial implementation of STAC. It currently supports Landsat-8 and Sentinel-2 imagery. The API allows these imagery libraries to be queried by any of the metadata fields such as date and time, location (by defining a polygon), bands, viewing angle, and so on. It returns GeoJSON. Matt provided some example queries.

May 16, 2018

There is a huge volume of satellite imagery that is being captured and has been captured over the past couple of decades. The challenge is to automatically find objects such as ships, trees, buildings, or containers in this vast amount if data and track changes in them over time. This morning at at this year's FOSS4GNA (Free and Open Source Software for Geospatial North America) get together in St Louis, Chris Holmes of Planet Labs, gave an insightful overview of the application of deep learning to geospatial data, identifying opportunities for the open source community to lead in the application of this data and technology to addressing this challenge.

Outside of Google, Planet Labs, DigitalGlobe and some others deep learning has been applied primarily to recognizing people in selfies and dogs and cats from peoples photos on online repositories such as Flickr. I have blogged previously about competitions DigitalGlobe (which has 100 petabytes of imagery) and Nvidia have hosted into automating building footprint and road network recognition. Planet Labs has a computer vision and analytics team that has been applying the results of academic research to geospatial data, primarily imagery from Planet's constellation of 150+ satellites. For example, they have used deep learning to detect building footprints and road networks in Dar es Salaam. Chris showed examples of airplane, ship and container recognition achieved using deep learning. But these are research pilots. One company has applied deep learning to scale for a practical application. Google has been developing and applying the technology to building recognition - automatically identifying building footprints and features such as aerials. The deep learning algorithms have been developed by academia and as a result the code for the most part is open source. For example, a deep neural network model developed originally for medical image segmentation called U-Net has been applied to identifying building footprints. Successful training of deep networks requires many thousand labeled training samples and this training data is not open source. Labeled data involves people on the ground manually ground-truthing building footprints and other features so that the deep learning algorithms can learn what to recognize.

Chris believed that it is time to open these models to a broader group. There are some technical things that need to happen to make this possible. We need new formats for exchanging raster and vector data. Cloud optimized GeoTiff (COG) makes it possible to stream imagery from cloud sources such as Amazon S3. Planet, Google Earth, and QGIS already support COGs and there are other open source tools such as COG-Explorer that support them as well. Chris and others are working on WFS 3.0, a completely new developer-friendly version of an existing OGC (Open Geospatial Consortium) standard for exchanging vector data over the web.

Another technical advance that is required is a common way to query satellite data based on date, time, location, type of bands, and so on. Chris and others have been working on a standard for searching satellite imagery for objects. The SpatioTemporal Asset Catalog, known as STAC, is an open specification that came about when fourteen different organizations came together to increase the interoperability of searching for satellite imagery. Currently when a user wants to search for all the imagery in their area and time of interest they can’t make just one search — they have to use different tools and connect to API’s that are similar but all slightly different. The STAC specification aims to make that much easier, by providing common metadata and API mechanics to search and access geospatial data. Chris emphasized that standards are essential for opening up satellite data to processing and visualization and encouraged the open source community to get involved with the OGC to further developer-friendly standards.

Currently in order to use imagery data, a lot of data preparation is required of the end user such as lining up pixels, assigning a projection, removing clouds and mist, and other corrections. We need to move toward analysis ready data where data derived from different sources can be combined for analysis.

Because of the huge volumes of computational intensity we need cloud-native engines to perform the analyses. Google, DigitalGlobe (Maxar), Planet have these deep learning engines, but Chris encouraged the open source community to get more involved in developing these engines.

An essential piece that is required to open up the data and technology to a broader audience is open labeled data, Chris referred to it as a "open labeled data commons". There are analogies with OpenStreetMap initiative. Chris suggested that something like this is required for labeled data - it needs a repository of open data, tools to capture and manage the data, and a community around this to capture and curate that data.

The other key piece is converting this data into actionable information. For example, dashboards that would alert municipal governments to un-permitted development, forestry enforcement to illegal forest cutting, or commodity price monitoring agencies to changes into the flow of ships into harbours or to large changes in containers at container ports.

Taken together Chris suggestions and work underway represent a roadmap for the open source geospatial community to lead in making the huge volumes of satellite imagery and other data queryable and able to generate actionable information.